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  Near-Maximum Entropy Models for Binary Neural Representations of Natural Images

Bethge, M., & Berens, P. (2008). Near-Maximum Entropy Models for Binary Neural Representations of Natural Images. In C. Platt, D. Koller, Y. Singer, & S. Roweis (Eds.), Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007 (pp. 97-104). Red Hook, NY, USA: Curran.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C74D-0 Version Permalink: https://hdl.handle.net/21.11116/0000-0003-389E-D
Genre: Conference Paper

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 Creators:
Bethge, M1, 2, Author           
Berens, P1, 2, Author           
Affiliations:
1Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Maximum entropy analysis of binary variables provides an elegant way for studying the role of pairwise correlations in neural populations. Unfortunately, these approaches suffer from their poor scalability to high dimensions. In sensory coding, however, high-dimensional data is ubiquitous. Here, we introduce a new approach using a near-maximum entropy model, that makes this type of analysis feasible for very high-dimensional data - the model parameters can be derived in closed form and sampling is easy. We demonstrate its usefulness by studying a simple neural representation model of natural images. For the first time, we are able to directly compare predictions from a pairwise maximum entropy model not only in small groups of neurons, but also in larger populations of more than thousand units. Our results indicate that in such larger networks interactions exist that are not predicted by pairwise correlations, despite the fact that pairwise correlations explain the lower-dimensional marginal statistics extrem ely well up to the limit of dimensionality where estimation of the full joint distribution is feasible.

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 Dates: 2008-09
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 4729
 Degree: -

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Title: Twenty-First Annual Conference on Neural Information Processing Systems (NIPS 2007)
Place of Event: Vancouver, BC, Canada
Start-/End Date: 2007-12-03 - 2007-12-06

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Title: Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007
Source Genre: Proceedings
 Creator(s):
Platt, C, Editor
Koller, D, Editor
Singer, Y, Editor
Roweis, ST, Editor
Affiliations:
-
Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 97 - 104 Identifier: ISBN: 978-1-605-60352-0